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Confidence Coefficient01:24

Confidence Coefficient

10.7K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

10.1K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
10.1K
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

11.7K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
11.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.9K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Econometric Views (EViews)01:29

Econometric Views (EViews)

582
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Video Experimental Relacionado

Updated: Feb 6, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

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Análisis de alta confianza de bloques diagonales para el reconocimiento de huellas palmares multivista en entornos

Shuping Zhao, Lunke Fei, Tingting Cai

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 4, 2026
    PubMed
    Resumen

    Este estudio presenta el análisis de alta confianza de bloques diagonales para el reconocimiento de huellas palmares multivista (HCBDA MPR) para mejorar la autenticación de identidad en entornos no controlados. El método mejora la precisión al garantizar una estructura de bloques diagonales de consenso en todas las vistas para la preservación robusta de características.

    Palabras clave:
    reconocimiento de huellas palmaresanálisis de bloques diagonalesaprendizaje multivistaautenticación de identidadentornos sin restricciones

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    Área de la Ciencia:

    • Ciencias de la Computación
    • Biometría
    • Reconocimiento de Patrones

    Sus antecedentes:

    • El reconocimiento de huellas palmares sin restricciones enfrenta desafíos debido a la calidad variable de la imagen, la iluminación y las poses en escenarios del mundo real.
    • Los métodos existentes a menudo se basan en estructuras de subespacio, y se han demostrado propiedades de bloques diagonales para datos de huellas palmares.

    Objetivo del estudio:

    • Desarrollar un modelo de aprendizaje unificado para el reconocimiento robusto de huellas palmares multivista.
    • Garantizar una propiedad de bloques diagonales de consenso en todas las vistas para mejorar la extracción de características.

    Principales métodos:

    • Se propuso un nuevo análisis de alta confianza de bloques diagonales para el reconocimiento de huellas palmares multivista (HCBDA MPR).
    • Se introdujo un regularizador de bloques diagonales multivista para imponer una estructura de bloques diagonales de consenso.
    • Se preservaron las características discriminatorias mientras se aprendía una estructura de bloques diagonales estricta en todas las vistas.

    Principales resultados:

    • El método propuesto HCBDA MPR demostró un rendimiento superior en bases de datos de huellas palmares sin restricciones del mundo real.
    • Se lograron las mayores precisiones de reconocimiento en comparación con los métodos existentes del estado del arte.
    • Se validó la efectividad de la propiedad de bloques diagonales de consenso para el reconocimiento de huellas palmares multivista.

    Conclusiones:

    • HCBDA MPR ofrece un avance significativo en el reconocimiento de huellas palmares sin restricciones.
    • El método aborda eficazmente los desafíos que plantean los entornos no controlados.
    • El enfoque proporciona un marco robusto para la autenticación de identidad utilizando huellas palmares multivista.